Transductive Non-linear Learning for Chinese Hypernym Prediction

Chengyu Wang, Junchi Yan, Aoying Zhou, Xiaofeng He


Abstract
Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc. Due to the flexibility of the Chinese language, it is challenging to identify hypernyms in Chinese accurately. Rather than extracting hypernyms from texts, in this paper, we present a transductive learning approach to establish mappings from entities to hypernyms in the embedding space directly. It combines linear and non-linear embedding projection models, with the capacity of encoding arbitrary language-specific rules. Experiments on real-world datasets illustrate that our approach outperforms previous methods for Chinese hypernym prediction.
Anthology ID:
P17-1128
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1394–1404
Language:
URL:
https://aclanthology.org/P17-1128
DOI:
10.18653/v1/P17-1128
Bibkey:
Cite (ACL):
Chengyu Wang, Junchi Yan, Aoying Zhou, and Xiaofeng He. 2017. Transductive Non-linear Learning for Chinese Hypernym Prediction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1394–1404, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Transductive Non-linear Learning for Chinese Hypernym Prediction (Wang et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1128.pdf